COMPMID-464 Implement Depthwise convolution 3x3 on NEON
Change-Id: Ie4e1803a52afac6b6c597c6e551729dad2347cd1
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/92607
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
diff --git a/src/core/NEON/kernels/NEDepthwiseConvolution3x3Kernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolution3x3Kernel.cpp
new file mode 100644
index 0000000..62aa934
--- /dev/null
+++ b/src/core/NEON/kernels/NEDepthwiseConvolution3x3Kernel.cpp
@@ -0,0 +1,186 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEDepthwiseConvolution3x3Kernel.h"
+#include "arm_compute/core/NEON/kernels/convolution/NEDirectConvolutionDetail.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/AccessWindowTranspose.h"
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/INEKernel.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+using namespace arm_compute;
+using namespace arm_compute::detail;
+
+NEDepthwiseConvolution3x3Kernel::NEDepthwiseConvolution3x3Kernel()
+ : _border_size(0), _input(), _output(), _weights(), _conv_info()
+{
+}
+
+BorderSize NEDepthwiseConvolution3x3Kernel::border_size() const
+{
+ return _border_size;
+}
+
+void NEDepthwiseConvolution3x3Kernel::configure(const ITensor *input, ITensor *output, const ITensor *weights, const PadStrideInfo &conv_info)
+{
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights);
+ ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 3 || weights->info()->dimension(1) != 3);
+
+ std::pair<unsigned int, unsigned int> expected_output = scaled_dimensions(input->info()->tensor_shape().x(), input->info()->tensor_shape().y(),
+ weights->info()->tensor_shape().x(), weights->info()->tensor_shape().y(),
+ conv_info);
+
+ ARM_COMPUTE_UNUSED(expected_output);
+ ARM_COMPUTE_ERROR_ON(expected_output.first != output->info()->tensor_shape().x());
+ ARM_COMPUTE_ERROR_ON(expected_output.second != output->info()->tensor_shape().y());
+
+ _input = input;
+ _output = output;
+ _weights = weights;
+ _conv_info = conv_info;
+ const unsigned int conv_stride_x = conv_info.stride().first;
+ const unsigned int conv_pad_x = conv_info.pad().first;
+ const unsigned int conv_pad_y = conv_info.pad().second;
+
+ ARM_COMPUTE_ERROR_ON(conv_stride_x < 1 || conv_stride_x > 3);
+
+ const unsigned int num_elems_written_per_iteration = 16 >> conv_stride_x;
+ _border_size = BorderSize(conv_pad_y, conv_pad_x);
+
+ // Configure kernel window
+ Window win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration));
+
+ AccessWindowStatic input_access(input->info(), -conv_pad_x, -conv_pad_y, input->info()->dimension(0) + _border_size.right, input->info()->dimension(1) + _border_size.bottom);
+ AccessWindowStatic weights_access(weights->info(), 0, 0, weights->info()->dimension(0), weights->info()->dimension(1));
+ AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+
+ update_window_and_padding(win, input_access, weights_access, output_access);
+ output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+ INEKernel::configure(win);
+}
+
+template <unsigned int stridex>
+class convolver_3x3
+{
+public:
+ static void convolve(const Window &window, unsigned int num_elems_written_per_iteration,
+ const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
+ {
+ const int input_stride_x = input->info()->strides_in_bytes().x();
+ const int input_stride_y = input->info()->strides_in_bytes().y();
+ const int output_stride_y = output->info()->strides_in_bytes().y();
+ const int kernel_stride_y = weights->info()->strides_in_bytes().y();
+ const int kernel_stride_z = weights->info()->strides_in_bytes().z();
+ const int output_w = output->info()->dimension(0);
+ const int output_h = output->info()->dimension(1);
+ const int delta_input = get_input_num_elems_processed<stridex>(num_elems_written_per_iteration);
+ const unsigned int conv_stride_y = std::get<1>(conv_info.stride());
+ const unsigned int conv_pad_x = std::get<0>(conv_info.pad());
+ const unsigned int conv_pad_y = std::get<1>(conv_info.pad());
+
+ // setup output window for the iterator
+ Window window_out = window;
+ window_out.set(Window::DimX, Window::Dimension(0, output->info()->dimension(Window::DimX), output->info()->dimension(Window::DimX)));
+ window_out.set(Window::DimY, Window::Dimension(0, output->info()->dimension(Window::DimY), output->info()->dimension(Window::DimY)));
+
+ // setup input window for the iterator
+ Window window_in = window;
+ // we just want execute_window_loop to iterate over the dimensions > 2, so we set the first 2 dimensions to 0
+ window_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+ window_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+ Window window_k = calculate_max_window(*weights->info(), Steps(1u));
+
+ Iterator in(input, window_in);
+ Iterator out(output, window_out);
+ Iterator w(weights, window_k);
+
+ const uint8_t *weights_ptr = w.ptr();
+
+ execute_window_loop(window_out, [&](const Coordinates & id)
+ {
+ const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y;
+ int ih = 0;
+ int oh = 0;
+
+ const uint8_t *ptr_weights_base = weights_ptr + id.z() * kernel_stride_z;
+ const auto ptr_weights_r0 = reinterpret_cast<const float *>(ptr_weights_base);
+ const auto ptr_weights_r1 = reinterpret_cast<const float *>(ptr_weights_base + kernel_stride_y);
+ const auto ptr_weights_r2 = reinterpret_cast<const float *>(ptr_weights_base + kernel_stride_y * 2);
+ const auto vw_r0 = load_matrix_row(ptr_weights_r0);
+ const auto vw_r1 = load_matrix_row(ptr_weights_r1);
+ const auto vw_r2 = load_matrix_row(ptr_weights_r2);
+
+ for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y)
+ {
+ auto in_top = reinterpret_cast<const float *>(input_ptr + (ih + 0) * input_stride_y);
+ auto in_mid = reinterpret_cast<const float *>(input_ptr + (ih + 1) * input_stride_y);
+ auto in_low = reinterpret_cast<const float *>(input_ptr + (ih + 2) * input_stride_y);
+ auto p_out = reinterpret_cast<float *>(out.ptr() + oh * output_stride_y);
+
+ for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration,
+ in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration)
+ {
+ auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, 0);
+ store_results<stridex>(p_out, vres);
+ }
+ }
+ },
+ in, out);
+ }
+};
+
+void NEDepthwiseConvolution3x3Kernel::run(const Window &window, const ThreadInfo &info)
+{
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_UNUSED(info);
+
+ const unsigned int conv_stride_x = _conv_info.stride().first;
+ const unsigned int num_elems_written_per_iteration = 16 >> conv_stride_x;
+
+ switch(conv_stride_x)
+ {
+ case 1:
+ convolver_3x3<1>::convolve(window, num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+ break;
+ case 2:
+ convolver_3x3<2>::convolve(window, num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+ break;
+ case 3:
+ convolver_3x3<3>::convolve(window, num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Not implemented");
+ }
+}
diff --git a/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp
index 60a3a1b..bf23c4b 100644
--- a/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp
@@ -22,6 +22,7 @@
* SOFTWARE.
*/
#include "arm_compute/core/NEON/kernels/NEDirectConvolutionLayerKernel.h"
+#include "arm_compute/core/NEON/kernels/convolution/NEDirectConvolutionDetail.h"
#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/Error.h"
@@ -37,6 +38,7 @@
#include <arm_neon.h>
using namespace arm_compute;
+using namespace arm_compute::detail;
namespace
{
@@ -428,122 +430,6 @@
};
#ifdef ARM_COMPUTE_AARCH64_V8_2
-inline float16x8x3_t load_matrix_row(const float16_t *ptr)
-{
- /* ptr is a pointer to a row in a 3x3 matrix, the function returns 3 vectors holding exactly the same value in all lanes:
- r.val[0] contains the first element, r.val[1] the second element and r.val[2] the third element (in all lanes) */
- const float16x8x3_t r =
- {
- {
- vld1q_dup_f16(ptr),
- vld1q_dup_f16(1 + ptr),
- vld1q_dup_f16(2 + ptr)
- }
- };
- return r;
-}
-
-template <unsigned int stridex>
-float16x8x2_t convolve_3x3(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low, const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2,
- int fixed_point_position);
-
-template <>
-float16x8x2_t convolve_3x3<1>(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low, const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2,
- int fixed_point_position)
-{
- ARM_COMPUTE_UNUSED(fixed_point_position);
-
- const float16x8x3_t vtop =
- {
- {
- vld1q_f16(in_top),
- vld1q_f16(in_top + 8),
- vld1q_f16(in_top + 16)
- }
- };
- const float16x8x3_t vmid =
- {
- {
- vld1q_f16(in_mid),
- vld1q_f16(in_mid + 8),
- vld1q_f16(in_mid + 16)
- }
- };
- const float16x8x3_t vlow =
- {
- {
- vld1q_f16(in_low),
- vld1q_f16(in_low + 8),
- vld1q_f16(in_low + 16)
- }
- };
- float16x8x2_t out =
- {
- {
- vmulq_f16(vtop.val[0], m0.val[0]),
- vmulq_f16(vtop.val[1], m0.val[0])
- }
- };
- out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vtop.val[0], vtop.val[1], 1), m0.val[1]));
- out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vtop.val[0], vtop.val[1], 2), m0.val[2]));
- out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vmid.val[0], m1.val[0]));
- out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vmid.val[0], vmid.val[1], 1), m1.val[1]));
- out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vmid.val[0], vmid.val[1], 2), m1.val[2]));
- out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vlow.val[0], m2.val[0]));
- out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vlow.val[0], vlow.val[1], 1), m2.val[1]));
- out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vlow.val[0], vlow.val[1], 2), m2.val[2]));
- out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vtop.val[1], vtop.val[2], 1), m0.val[1]));
- out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vtop.val[1], vtop.val[2], 2), m0.val[2]));
- out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vmid.val[1], m1.val[0]));
- out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vmid.val[1], vmid.val[2], 1), m1.val[1]));
- out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vmid.val[1], vmid.val[2], 2), m1.val[2]));
- out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vlow.val[1], m2.val[0]));
- out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vlow.val[1], vlow.val[2], 1), m2.val[1]));
- out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vlow.val[1], vlow.val[2], 2), m2.val[2]));
- return out;
-}
-
-template <>
-inline float16x8x2_t convolve_3x3<2>(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low, const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2,
- int fixed_point_position)
-{
- float16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position);
- out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 2), out.val[0], 1);
- out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 0), out.val[0], 2);
- out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 2), out.val[0], 3);
- return out;
-}
-
-template <>
-inline float16x8x2_t convolve_3x3<3>(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low, const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2,
- int fixed_point_position)
-{
- float16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position);
- out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 3), out.val[0], 1);
- return out;
-}
-
-template <unsigned int stridex>
-void store_results(float16_t *buffer, const float16x8x2_t &values);
-
-template <>
-void store_results<1>(float16_t *buffer, const float16x8x2_t &values)
-{
- vst1q_f16(buffer, values.val[0]);
- vst1q_f16(buffer + 8, values.val[1]);
-}
-
-template <>
-void store_results<2>(float16_t *buffer, const float16x8x2_t &values)
-{
- vst1q_f16(buffer, values.val[0]);
-}
-
-template <>
-void store_results<3>(float16_t *buffer, const float16x8x2_t &values)
-{
- vst1_f16(buffer, vget_low_f16(values.val[0]));
-}
template <unsigned int stridex>
void accumulate_results(float16_t *buffer, const float16x8x2_t &values);
@@ -569,33 +455,6 @@
#endif /* ARM_COMPUTE_AARCH64_V8_2 */
-inline float32x4x3_t load_matrix_row(const float *ptr)
-{
- const float32x4x3_t r =
- {
- {
- vld1q_dup_f32(ptr),
- vld1q_dup_f32(1 + ptr),
- vld1q_dup_f32(2 + ptr)
- }
- };
- return r;
-}
-inline qint8x8x3_t load_matrix_row(const qint8_t *ptr)
-{
- /* ptr is a pointer to a row in a 3x3 matrix, the function returns 3 vectors holding exactly the same value in all lanes:
- r.val[0] contains the first element, r.val[1] the second element and r.val[2] the third element (in all lanes) */
- const qint8x8x3_t r =
- {
- {
- vld1_dup_qs8(ptr),
- vld1_dup_qs8(1 + ptr),
- vld1_dup_qs8(2 + ptr)
- }
- };
- return r;
-}
-
template <unsigned int stridex>
float32x4x2_t convolve_5x5(const float *in_0, const float *in_1, const float *in_2, const float *in_3, const float *in_4,
const float *m0, const float *m1, const float *m2, const float *m3, const float *m4, int fixed_point_position);
@@ -750,213 +609,6 @@
}
template <unsigned int stridex>
-float32x4x2_t convolve_3x3(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position);
-
-template <>
-inline float32x4x2_t convolve_3x3<1>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position)
-{
- ARM_COMPUTE_UNUSED(fixed_point_position);
-
- const float32x4x3_t vtop =
- {
- {
- vld1q_f32(in_top),
- vld1q_f32(in_top + 4),
- vld1q_f32(in_top + 8)
- }
- };
- const float32x4x3_t vmid =
- {
- {
- vld1q_f32(in_mid),
- vld1q_f32(in_mid + 4),
- vld1q_f32(in_mid + 8)
- }
- };
- const float32x4x3_t vlow =
- {
- {
- vld1q_f32(in_low),
- vld1q_f32(in_low + 4),
- vld1q_f32(in_low + 8)
- }
- };
- float32x4x2_t out =
- {
- {
- vmulq_f32(vtop.val[0], m0.val[0]),
- vmulq_f32(vtop.val[1], m0.val[0])
- }
- };
- out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vtop.val[0], vtop.val[1], 1), m0.val[1]);
- out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vtop.val[0], vtop.val[1], 2), m0.val[2]);
-
- out.val[0] = vmlaq_f32(out.val[0], vmid.val[0], m1.val[0]);
- out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vmid.val[0], vmid.val[1], 1), m1.val[1]);
- out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vmid.val[0], vmid.val[1], 2), m1.val[2]);
-
- out.val[0] = vmlaq_f32(out.val[0], vlow.val[0], m2.val[0]);
- out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vlow.val[0], vlow.val[1], 1), m2.val[1]);
- out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vlow.val[0], vlow.val[1], 2), m2.val[2]);
-
- out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vtop.val[1], vtop.val[2], 1), m0.val[1]);
- out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vtop.val[1], vtop.val[2], 2), m0.val[2]);
-
- out.val[1] = vmlaq_f32(out.val[1], vmid.val[1], m1.val[0]);
- out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vmid.val[1], vmid.val[2], 1), m1.val[1]);
- out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vmid.val[1], vmid.val[2], 2), m1.val[2]);
-
- out.val[1] = vmlaq_f32(out.val[1], vlow.val[1], m2.val[0]);
- out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vlow.val[1], vlow.val[2], 1), m2.val[1]);
- out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vlow.val[1], vlow.val[2], 2), m2.val[2]);
- return out;
-}
-
-template <>
-inline float32x4x2_t convolve_3x3<2>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position)
-{
- float32x4x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position);
- out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 2), out.val[0], 1);
- out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 0), out.val[0], 2);
- out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 2), out.val[0], 3);
- return out;
-}
-
-template <>
-inline float32x4x2_t convolve_3x3<3>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position)
-{
- float32x4x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position);
- out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 3), out.val[0], 1);
- return out;
-}
-
-template <unsigned int stridex>
-qint16x8x2_t convolve_3x3(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position);
-
-template <>
-inline qint16x8x2_t convolve_3x3<1>(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position)
-{
- ARM_COMPUTE_UNUSED(fixed_point_position);
-
- const qint8x8x3_t vtop =
- {
- {
- vld1_qs8(in_top),
- vld1_qs8(in_top + 8),
- vld1_qs8(in_top + 16)
- }
- };
- const qint8x8x3_t vmid =
- {
- {
- vld1_qs8(in_mid),
- vld1_qs8(in_mid + 8),
- vld1_qs8(in_mid + 16)
- }
- };
- const qint8x8x3_t vlow =
- {
- {
- vld1_qs8(in_low),
- vld1_qs8(in_low + 8),
- vld1_qs8(in_low + 16)
- }
- };
- qint16x8x2_t out =
- {
- {
- vmull_qs8(vtop.val[0], m0.val[0], fixed_point_position),
- vmull_qs8(vtop.val[1], m0.val[0], fixed_point_position)
- }
- };
- out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vtop.val[0], vtop.val[1], 1), m0.val[1], fixed_point_position);
- out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vtop.val[0], vtop.val[1], 2), m0.val[2], fixed_point_position);
- out.val[0] = vqmlal_qs8(out.val[0], vmid.val[0], m1.val[0], fixed_point_position);
- out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vmid.val[0], vmid.val[1], 1), m1.val[1], fixed_point_position);
- out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vmid.val[0], vmid.val[1], 2), m1.val[2], fixed_point_position);
- out.val[0] = vqmlal_qs8(out.val[0], vlow.val[0], m2.val[0], fixed_point_position);
- out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vlow.val[0], vlow.val[1], 1), m2.val[1], fixed_point_position);
- out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vlow.val[0], vlow.val[1], 2), m2.val[2], fixed_point_position);
- out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vtop.val[1], vtop.val[2], 1), m0.val[1], fixed_point_position);
- out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vtop.val[1], vtop.val[2], 2), m0.val[2], fixed_point_position);
- out.val[1] = vqmlal_qs8(out.val[1], vmid.val[1], m1.val[0], fixed_point_position);
- out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vmid.val[1], vmid.val[2], 1), m1.val[1], fixed_point_position);
- out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vmid.val[1], vmid.val[2], 2), m1.val[2], fixed_point_position);
- out.val[1] = vqmlal_qs8(out.val[1], vlow.val[1], m2.val[0], fixed_point_position);
- out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vlow.val[1], vlow.val[2], 1), m2.val[1], fixed_point_position);
- out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vlow.val[1], vlow.val[2], 2), m2.val[2], fixed_point_position);
- return out;
-}
-
-template <>
-inline qint16x8x2_t convolve_3x3<2>(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position)
-{
- qint16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position);
- out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 2), out.val[0], 1);
- out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 4), out.val[0], 2);
- out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 6), out.val[0], 3);
- out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 0), out.val[0], 4);
- out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 2), out.val[0], 5);
- out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 4), out.val[0], 6);
- out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 6), out.val[0], 7);
- return out;
-}
-
-template <>
-inline qint16x8x2_t convolve_3x3<3>(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position)
-{
- qint16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position);
- out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 3), out.val[0], 1);
- out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 6), out.val[0], 2);
- out.val[0] = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 1), out.val[0], 3);
- return out;
-}
-
-template <unsigned int stridex>
-void store_results(float *buffer, const float32x4x2_t &values);
-
-template <>
-void store_results<1>(float *buffer, const float32x4x2_t &values)
-{
- vst1q_f32(buffer, values.val[0]);
- vst1q_f32(buffer + 4, values.val[1]);
-}
-
-template <>
-void store_results<2>(float *buffer, const float32x4x2_t &values)
-{
- vst1q_f32(buffer, values.val[0]);
-}
-
-template <>
-void store_results<3>(float *buffer, const float32x4x2_t &values)
-{
- vst1_f32(buffer, vget_low_f32(values.val[0]));
-}
-
-template <unsigned int stridex>
-void store_results(qint16_t *buffer, const qint16x8x2_t &values);
-
-template <>
-void store_results<1>(qint16_t *buffer, const qint16x8x2_t &values)
-{
- vst1q_qs16(buffer, values.val[0]);
- vst1q_qs16(buffer + 8, values.val[1]);
-}
-
-template <>
-void store_results<2>(qint16_t *buffer, const qint16x8x2_t &values)
-{
- vst1q_qs16(buffer, values.val[0]);
-}
-
-template <>
-void store_results<3>(qint16_t *buffer, const qint16x8x2_t &values)
-{
- vst1_qs16(buffer, vget_low_s16(values.val[0]));
-}
-
-template <unsigned int stridex>
void accumulate_results(float *buffer, const float32x4x2_t &values);
template <>
@@ -1000,27 +652,6 @@
vst1_qs16(buffer, vqadd_qs16(vld1_qs16(buffer), vget_low_s16(values.val[0])));
}
-template <unsigned int stridex>
-int get_input_num_elems_processed(unsigned int num_elems_written_per_iteration);
-
-template <>
-int get_input_num_elems_processed<1>(unsigned int num_elems_written_per_iteration)
-{
- return num_elems_written_per_iteration;
-}
-
-template <>
-int get_input_num_elems_processed<2>(unsigned int num_elems_written_per_iteration)
-{
- return num_elems_written_per_iteration << 1;
-}
-
-template <>
-int get_input_num_elems_processed<3>(unsigned int num_elems_written_per_iteration)
-{
- return num_elems_written_per_iteration * 3;
-}
-
template <typename T1, typename T2, unsigned int stridex>
class convolver_3x3
{